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<h1> Feed-forward Neural Networks and Multinomial Log-Linear Models
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</div><h2>Documentation for package &lsquo;nnet&rsquo; version 7.3-5</h2>

<ul><li><a href="../DESCRIPTION">DESCRIPTION file</a>.</li>
<li><a href="../NEWS">Package NEWS</a>.</li>
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<h2>Help Pages</h2>


<table width="100%">
<tr><td width="25%"><a href="nnet.html">add.net</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="multinom.html">add1.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="multinom.html">anova.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="class.ind.html">class.ind</a></td>
<td>Generates Class Indicator Matrix from a Factor</td></tr>
<tr><td width="25%"><a href="multinom.html">coef.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="nnet.html">coef.nnet</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="multinom.html">drop1.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="nnet.html">eval.nn</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="multinom.html">extractAIC.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="multinom.html">logLik.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="multinom.html">model.frame.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="multinom.html">multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="nnet.html">nnet</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="nnet.html">nnet.default</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="nnet.html">nnet.formula</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="nnet.Hess.html">nnetHess</a></td>
<td>Evaluates Hessian for a Neural Network</td></tr>
<tr><td width="25%"><a href="nnet.html">norm.net</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="multinom.html">predict.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="predict.nnet.html">predict.nnet</a></td>
<td>Predict New Examples by a Trained Neural Net</td></tr>
<tr><td width="25%"><a href="multinom.html">print.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="nnet.html">print.nnet</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="multinom.html">print.summary.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="nnet.html">print.summary.nnet</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="multinom.html">summary.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="nnet.html">summary.nnet</a></td>
<td>Fit Neural Networks</td></tr>
<tr><td width="25%"><a href="multinom.html">vcov.multinom</a></td>
<td>Fit Multinomial Log-linear Models</td></tr>
<tr><td width="25%"><a href="which.is.max.html">which.is.max</a></td>
<td>Find Maximum Position in Vector</td></tr>
</table>
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